Text Generation
Transformers
Safetensors
llama
causal-lm
code
fill-in-the-middle
research
experimental
text-generation-inference
Instructions to use mossez-systems/Mossez-100M-Coder-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mossez-systems/Mossez-100M-Coder-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mossez-systems/Mossez-100M-Coder-Base")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mossez-systems/Mossez-100M-Coder-Base") model = AutoModelForCausalLM.from_pretrained("mossez-systems/Mossez-100M-Coder-Base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use mossez-systems/Mossez-100M-Coder-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mossez-systems/Mossez-100M-Coder-Base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mossez-systems/Mossez-100M-Coder-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mossez-systems/Mossez-100M-Coder-Base
- SGLang
How to use mossez-systems/Mossez-100M-Coder-Base with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "mossez-systems/Mossez-100M-Coder-Base" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mossez-systems/Mossez-100M-Coder-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "mossez-systems/Mossez-100M-Coder-Base" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mossez-systems/Mossez-100M-Coder-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mossez-systems/Mossez-100M-Coder-Base with Docker Model Runner:
docker model run hf.co/mossez-systems/Mossez-100M-Coder-Base
Evaluation
The selected model was evaluated against its published Base source using frozen packed validation/test data plus completion and FIM slices.
| Metric | Published Base loss | Coder-Base loss | Relative change |
|---|---|---|---|
| Packed validation | 2.572834 | 1.488147 | -42.16% |
| Packed test | 2.604606 | 1.798295 | -30.96% |
| Completion validation | 2.286497 | 1.521212 | -33.47% |
| FIM validation | 2.408383 | 1.394937 | -42.08% |
All four frozen loss metrics improved. These tests measure a narrow local code corpus and do not establish correctness on HumanEval, MBPP, repository-level work, security tasks, or long-context generation.